def test_score_sequence(self): # Network predicts <unk> probability. scorer = TextScorer(self.dummy_network) word_ids = numpy.arange(6) class_ids = numpy.arange(6) membership_probs = numpy.ones(6, dtype='float32') logprob = scorer.score_sequence(word_ids, class_ids, membership_probs) correct = word_ids[1:].astype('float32') correct = correct / 5 correct = numpy.log(correct).sum() self.assertAlmostEqual(logprob, correct, places=5) # <unk> is removed from the resulting logprobs. scorer = TextScorer(self.dummy_network, ignore_unk=True) word_ids = numpy.arange(6) word_ids[3] = self.vocabulary.word_to_id['<unk>'] class_ids = numpy.arange(6) membership_probs = numpy.ones(6, dtype='float32') logprob = scorer.score_sequence(word_ids, class_ids, membership_probs) correct = word_ids[[1, 2, 4, 5]].astype('float32') correct = correct / 5 correct = numpy.log(correct).sum() self.assertAlmostEqual(logprob, correct, places=5) # <unk> is assigned a constant logprob. scorer = TextScorer(self.dummy_network, ignore_unk=False, unk_penalty=-5) word_ids = numpy.arange(6) word_ids[3] = self.vocabulary.word_to_id['<unk>'] class_ids = numpy.arange(6) membership_probs = numpy.ones(6, dtype='float32') logprob = scorer.score_sequence(word_ids, class_ids, membership_probs) correct = word_ids[[1, 2, 4, 5]].astype('float32') correct = correct / 5 correct = numpy.log(correct).sum() - 5 self.assertAlmostEqual(logprob, correct, places=5)
def score(args): """A function that performs the "theanolm score" command. :type args: argparse.Namespace :param args: a collection of command line arguments """ log_file = args.log_file log_level = getattr(logging, args.log_level.upper(), None) if not isinstance(log_level, int): print("Invalid logging level requested:", args.log_level) sys.exit(1) log_format = '%(asctime)s %(funcName)s: %(message)s' if args.log_file == '-': logging.basicConfig(stream=sys.stdout, format=log_format, level=log_level) else: logging.basicConfig(filename=log_file, format=log_format, level=log_level) if args.debug: theano.config.compute_test_value = 'warn' logging.info("Enabled computing test values for tensor variables.") logging.warning("GpuArray backend will fail random number generation!") else: theano.config.compute_test_value = 'off' theano.config.profile = args.profile theano.config.profile_memory = args.profile default_device = get_default_device(args.default_device) network = Network.from_file(args.model_path, exclude_unk=args.exclude_unk, default_device=default_device) logging.info("Building text scorer.") scorer = TextScorer(network, args.shortlist, args.exclude_unk, args.profile) logging.info("Scoring text.") if args.output == 'perplexity': _score_text(args.input_file, network.vocabulary, scorer, args.output_file, args.log_base, args.subwords, False) elif args.output == 'word-scores': _score_text(args.input_file, network.vocabulary, scorer, args.output_file, args.log_base, args.subwords, True) elif args.output == 'utterance-scores': _score_utterances(args.input_file, network.vocabulary, scorer, args.output_file, args.log_base) else: print("Invalid output format requested:", args.output) sys.exit(1)
def test_score_batch(self): # Network predicts <unk> probability. scorer = TextScorer(self.dummy_network) word_ids = numpy.arange(6).reshape((3, 2)) class_ids = numpy.arange(6).reshape((3, 2)) membership_probs = numpy.ones_like(word_ids).astype('float32') mask = numpy.ones_like(word_ids) logprobs = scorer.score_batch(word_ids, class_ids, membership_probs, mask) assert_almost_equal(logprobs[0], numpy.log(word_ids[1:, 0].astype('float32') / 5)) assert_almost_equal(logprobs[1], numpy.log(word_ids[1:, 1].astype('float32') / 5)) # <unk> is removed from the resulting logprobs. scorer = TextScorer(self.dummy_network, ignore_unk=True) word_ids = numpy.arange(6).reshape((3, 2)) word_ids[1, 1] = self.vocabulary.word_to_id['<unk>'] class_ids = numpy.arange(6).reshape((3, 2)) membership_probs = numpy.ones_like(word_ids).astype('float32') mask = numpy.ones_like(word_ids) logprobs = scorer.score_batch(word_ids, class_ids, membership_probs, mask) assert_almost_equal(logprobs[0], numpy.log(word_ids[1:, 0].astype('float32') / 5)) assert_almost_equal(logprobs[1], numpy.log(word_ids[2:, 1].astype('float32') / 5)) # <unk> is assigned a constant logprob. scorer = TextScorer(self.dummy_network, ignore_unk=False, unk_penalty=-5) word_ids = numpy.arange(6).reshape((3, 2)) word_ids[1, 1] = self.vocabulary.word_to_id['<unk>'] class_ids = numpy.arange(6).reshape((3, 2)) membership_probs = numpy.ones_like(word_ids).astype('float32') mask = numpy.ones_like(word_ids) logprobs = scorer.score_batch(word_ids, class_ids, membership_probs, mask) assert_almost_equal(logprobs[0], numpy.log(word_ids[1:, 0].astype('float32') / 5)) assert_almost_equal(logprobs[1][0], -5) assert_almost_equal(logprobs[1][1], numpy.log(word_ids[2, 1].astype('float32') / 5))
def test_score_sequence(self): # Network predicts <unk> probability. scorer = TextScorer(self.dummy_network, use_shortlist=False) word_ids = numpy.arange(15) class_ids, _ = self.vocabulary.get_class_memberships(word_ids) membership_probs = numpy.ones_like(word_ids).astype('float32') logprob = scorer.score_sequence(word_ids, class_ids, membership_probs) correct = word_ids[1:].astype('float32') correct /= 100.0 correct[12] = 12.0 / 100.0 correct[13] = 12.0 / 100.0 correct = numpy.log(correct).sum() self.assertAlmostEqual(logprob, correct, places=4) # Network predicts <unk> probability. This is distributed for # out-of-shortlist words according to word frequency. scorer = TextScorer(self.dummy_network, use_shortlist=True) word_ids = numpy.arange(15) class_ids, _ = self.vocabulary.get_class_memberships(word_ids) membership_probs = numpy.ones_like(word_ids).astype('float32') logprob = scorer.score_sequence(word_ids, class_ids, membership_probs) correct = word_ids[1:].astype('float32') correct /= 100.0 correct[11] = 1.0 # <unk> is ignored correct[12] = 12.0 / 100.0 * 0.3 correct[13] = 12.0 / 100.0 * 0.7 correct = numpy.log(correct).sum() self.assertAlmostEqual(logprob, correct, places=5) # OOV and OOS words are excluded from the resulting logprobs. scorer = TextScorer(self.dummy_network, use_shortlist=False, exclude_unk=True) word_ids = numpy.arange(15) class_ids, _ = self.vocabulary.get_class_memberships(word_ids) membership_probs = numpy.ones_like(word_ids).astype('float32') logprob = scorer.score_sequence(word_ids, class_ids, membership_probs) correct = word_ids[1:12].astype('float32') correct /= 100.0 correct = numpy.log(correct).sum() self.assertAlmostEqual(logprob, correct, places=5)
def score(args): with h5py.File(args.model_path, 'r') as state: print("Reading vocabulary from network state.") sys.stdout.flush() vocabulary = Vocabulary.from_state(state) print("Number of words in vocabulary:", vocabulary.num_words()) print("Number of word classes:", vocabulary.num_classes()) print("Building neural network.") sys.stdout.flush() architecture = Architecture.from_state(state) network = Network(vocabulary, architecture) print("Restoring neural network state.") sys.stdout.flush() network.set_state(state) print("Building text scorer.") sys.stdout.flush() if args.unk_penalty is None: ignore_unk = False unk_penalty = None elif args.unk_penalty == 0: ignore_unk = True unk_penalty = None else: ignore_unk = False unk_penalty = args.unk_penalty scorer = TextScorer(network, ignore_unk, unk_penalty) print("Scoring text.") if args.output == 'perplexity': _score_text(args.input_file, vocabulary, scorer, args.output_file, args.log_base, False) elif args.output == 'word-scores': _score_text(args.input_file, vocabulary, scorer, args.output_file, args.log_base, True) elif args.output == 'utterance-scores': _score_utterances(args.input_file, vocabulary, scorer, args.output_file, args.log_base)
def test_score_batch(self): # Network predicts <unk> probability. scorer = TextScorer(self.dummy_network) word_ids = numpy.arange(6).reshape((3, 2)) class_ids = numpy.arange(6).reshape((3, 2)) membership_probs = numpy.ones_like(word_ids).astype('float32') mask = numpy.ones_like(word_ids) logprobs = scorer.score_batch(word_ids, class_ids, membership_probs, mask) assert_almost_equal(logprobs[0], numpy.log(word_ids[1:,0].astype('float32') / 5)) assert_almost_equal(logprobs[1], numpy.log(word_ids[1:,1].astype('float32') / 5)) # <unk> is removed from the resulting logprobs. scorer = TextScorer(self.dummy_network, ignore_unk=True) word_ids = numpy.arange(6).reshape((3, 2)) word_ids[1,1] = self.vocabulary.word_to_id['<unk>'] class_ids = numpy.arange(6).reshape((3, 2)) membership_probs = numpy.ones_like(word_ids).astype('float32') mask = numpy.ones_like(word_ids) logprobs = scorer.score_batch(word_ids, class_ids, membership_probs, mask) assert_almost_equal(logprobs[0], numpy.log(word_ids[1:,0].astype('float32') / 5)) assert_almost_equal(logprobs[1], numpy.log(word_ids[2:,1].astype('float32') / 5)) # <unk> is assigned a constant logprob. scorer = TextScorer(self.dummy_network, ignore_unk=False, unk_penalty=-5) word_ids = numpy.arange(6).reshape((3, 2)) word_ids[1,1] = self.vocabulary.word_to_id['<unk>'] class_ids = numpy.arange(6).reshape((3, 2)) membership_probs = numpy.ones_like(word_ids).astype('float32') mask = numpy.ones_like(word_ids) logprobs = scorer.score_batch(word_ids, class_ids, membership_probs, mask) assert_almost_equal(logprobs[0], numpy.log(word_ids[1:,0].astype('float32') / 5)) assert_almost_equal(logprobs[1][0], -5) assert_almost_equal(logprobs[1][1], numpy.log(word_ids[2,1].astype('float32') / 5))
def train(args): """A function that performs the "theanolm train" command. :type args: argparse.Namespace :param args: a collection of command line arguments """ numpy.random.seed(args.random_seed) log_file = args.log_file log_level = getattr(logging, args.log_level.upper(), None) if not isinstance(log_level, int): print("Invalid logging level requested:", args.log_level) sys.exit(1) log_format = '%(asctime)s %(funcName)s: %(message)s' if args.log_file == '-': logging.basicConfig(stream=sys.stdout, format=log_format, level=log_level) else: logging.basicConfig(filename=log_file, format=log_format, level=log_level) if args.debug: theano.config.compute_test_value = 'warn' print("Enabled computing test values for tensor variables.") print("Warning: GpuArray backend will fail random number generation!") else: theano.config.compute_test_value = 'off' theano.config.profile = args.profile theano.config.profile_memory = args.profile with h5py.File(args.model_path, 'a', driver='core') as state: vocabulary = _read_vocabulary(args, state) if args.num_noise_samples > vocabulary.num_classes(): print("Number of noise samples ({}) is larger than the number of " "classes. This doesn't make sense and would cause sampling " "to fail.".format(args.num_noise_samples)) sys.exit(1) num_training_files = len(args.training_set) if len(args.weights) > num_training_files: print("You specified more weights than training files.") sys.exit(1) weights = numpy.ones(num_training_files).astype(theano.config.floatX) for index, weight in enumerate(args.weights): weights[index] = weight training_options = { 'batch_size': args.batch_size, 'sequence_length': args.sequence_length, 'validation_frequency': args.validation_frequency, 'patience': args.patience, 'stopping_criterion': args.stopping_criterion, 'max_epochs': args.max_epochs, 'min_epochs': args.min_epochs, 'max_annealing_count': args.max_annealing_count } logging.debug("TRAINING OPTIONS") for option_name, option_value in training_options.items(): logging.debug("%s: %s", option_name, str(option_value)) optimization_options = { 'method': args.optimization_method, 'epsilon': args.numerical_stability_term, 'gradient_decay_rate': args.gradient_decay_rate, 'sqr_gradient_decay_rate': args.sqr_gradient_decay_rate, 'learning_rate': args.learning_rate, 'weights': weights, 'momentum': args.momentum, 'max_gradient_norm': args.gradient_normalization, 'cost_function': args.cost, 'num_noise_samples': args.num_noise_samples, 'noise_sharing': args.noise_sharing, 'exclude_unk': args.exclude_unk } logging.debug("OPTIMIZATION OPTIONS") for option_name, option_value in optimization_options.items(): if isinstance(option_value, list): value_str = ', '.join(str(x) for x in option_value) logging.debug("%s: [%s]", option_name, value_str) else: logging.debug("%s: %s", option_name, str(option_value)) if len(args.sampling) > len(args.training_set): print("You specified more sampling coefficients than training " "files.") sys.exit(1) print("Creating trainer.") sys.stdout.flush() trainer = Trainer(training_options, vocabulary, args.training_set, args.sampling) trainer.set_logging(args.log_interval) print("Building neural network.") sys.stdout.flush() if args.architecture == 'lstm300' or args.architecture == 'lstm1500': architecture = Architecture.from_package(args.architecture) else: with open(args.architecture, 'rt', encoding='utf-8') as arch_file: architecture = Architecture.from_description(arch_file) network = Network(architecture, vocabulary, trainer.class_prior_probs, args.noise_dampening, default_device=args.default_device, profile=args.profile) print("Compiling optimization function.") sys.stdout.flush() optimizer = create_optimizer(optimization_options, network, profile=args.profile) if args.print_graph: print("Cost function computation graph:") theano.printing.debugprint(optimizer.gradient_update_function) trainer.initialize(network, state, optimizer) # XXX Write the model instantly back to disk. Just adds word unigram # counts. This is a temporary hack. Remove at some point. trainer.get_state(state) state.flush() # XXX if args.validation_file is not None: print("Building text scorer for cross-validation.") sys.stdout.flush() scorer = TextScorer(network, use_shortlist=True, exclude_unk=args.exclude_unk, profile=args.profile) print("Validation text:", args.validation_file.name) validation_mmap = mmap.mmap(args.validation_file.fileno(), 0, prot=mmap.PROT_READ) validation_iter = \ LinearBatchIterator(validation_mmap, vocabulary, batch_size=args.batch_size, max_sequence_length=args.sequence_length, map_oos_to_unk=False) trainer.set_validation(validation_iter, scorer) else: print("Cross-validation will not be performed.") validation_iter = None print("Training neural network.") sys.stdout.flush() trainer.train() if 'layers' not in state.keys(): print("The model has not been trained. No cross-validations were " "performed or training did not improve the model.") elif validation_iter is not None: network.set_state(state) perplexity = scorer.compute_perplexity(validation_iter) print("Best validation set perplexity:", perplexity)
def train(args): numpy.random.seed(args.random_seed) log_file = args.log_file log_level = getattr(logging, args.log_level.upper(), None) if not isinstance(log_level, int): print("Invalid logging level requested:", args.log_level) sys.exit(1) log_format = "%(asctime)s %(funcName)s: %(message)s" if args.log_file == "-": logging.basicConfig(stream=sys.stdout, format=log_format, level=log_level) else: logging.basicConfig(filename=log_file, format=log_format, level=log_level) if args.debug: theano.config.compute_test_value = "warn" print("Enabled computing test values for tensor variables.") print("Warning: GpuArray backend will fail random number generation!") else: theano.config.compute_test_value = "off" theano.config.profile = args.profile theano.config.profile_memory = args.profile with h5py.File(args.model_path, "a", driver="core") as state: if state.keys(): print("Reading vocabulary from existing network state.") sys.stdout.flush() vocabulary = Vocabulary.from_state(state) elif args.vocabulary is None: print("Constructing vocabulary from training set.") sys.stdout.flush() vocabulary = Vocabulary.from_corpus(args.training_set, args.num_classes) for training_file in args.training_set: training_file.seek(0) vocabulary.get_state(state) else: print("Reading vocabulary from {}.".format(args.vocabulary)) sys.stdout.flush() with open(args.vocabulary, "rt", encoding="utf-8") as vocab_file: vocabulary = Vocabulary.from_file(vocab_file, args.vocabulary_format) if args.vocabulary_format == "classes": print("Computing class membership probabilities from " "unigram word counts.") sys.stdout.flush() vocabulary.compute_probs(args.training_set) vocabulary.get_state(state) print("Number of words in vocabulary:", vocabulary.num_words()) print("Number of word classes:", vocabulary.num_classes()) if args.num_noise_samples > vocabulary.num_classes(): print( "Number of noise samples ({}) is larger than the number of " "classes. This doesn't make sense and would cause sampling " "to fail.".format(args.num_noise_samples) ) sys.exit(1) if args.unk_penalty is None: ignore_unk = False unk_penalty = None elif args.unk_penalty == 0: ignore_unk = True unk_penalty = None else: ignore_unk = False unk_penalty = args.unk_penalty num_training_files = len(args.training_set) if len(args.weights) > num_training_files: print("You specified more weights than training files.") sys.exit(1) weights = numpy.ones(num_training_files).astype(theano.config.floatX) for index, weight in enumerate(args.weights): weights[index] = weight training_options = { "batch_size": args.batch_size, "sequence_length": args.sequence_length, "validation_frequency": args.validation_frequency, "patience": args.patience, "stopping_criterion": args.stopping_criterion, "max_epochs": args.max_epochs, "min_epochs": args.min_epochs, "max_annealing_count": args.max_annealing_count, } logging.debug("TRAINING OPTIONS") for option_name, option_value in training_options.items(): logging.debug("%s: %s", option_name, str(option_value)) optimization_options = { "method": args.optimization_method, "epsilon": args.numerical_stability_term, "gradient_decay_rate": args.gradient_decay_rate, "sqr_gradient_decay_rate": args.sqr_gradient_decay_rate, "learning_rate": args.learning_rate, "weights": weights, "momentum": args.momentum, "max_gradient_norm": args.gradient_normalization, "cost_function": args.cost, "num_noise_samples": args.num_noise_samples, "noise_sharing": args.noise_sharing, "ignore_unk": ignore_unk, "unk_penalty": unk_penalty, } logging.debug("OPTIMIZATION OPTIONS") for option_name, option_value in optimization_options.items(): if type(option_value) is list: value_str = ", ".join(str(x) for x in option_value) logging.debug("%s: [%s]", option_name, value_str) else: logging.debug("%s: %s", option_name, str(option_value)) if len(args.sampling) > len(args.training_set): print("You specified more sampling coefficients than training " "files.") sys.exit(1) print("Creating trainer.") sys.stdout.flush() trainer = Trainer(training_options, vocabulary, args.training_set, args.sampling) trainer.set_logging(args.log_interval) print("Building neural network.") sys.stdout.flush() if args.architecture == "lstm300" or args.architecture == "lstm1500": architecture = Architecture.from_package(args.architecture) else: with open(args.architecture, "rt", encoding="utf-8") as arch_file: architecture = Architecture.from_description(arch_file) network = Network( architecture, vocabulary, trainer.class_prior_probs, args.noise_dampening, default_device=args.default_device, profile=args.profile, ) print("Compiling optimization function.") sys.stdout.flush() optimizer = create_optimizer(optimization_options, network, device=args.default_device, profile=args.profile) if args.print_graph: print("Cost function computation graph:") theano.printing.debugprint(optimizer.gradient_update_function) trainer.initialize(network, state, optimizer) if not args.validation_file is None: print("Building text scorer for cross-validation.") sys.stdout.flush() scorer = TextScorer(network, ignore_unk, unk_penalty, args.profile) print("Validation text:", args.validation_file.name) validation_mmap = mmap.mmap(args.validation_file.fileno(), 0, prot=mmap.PROT_READ) validation_iter = LinearBatchIterator( validation_mmap, vocabulary, batch_size=args.batch_size, max_sequence_length=None ) trainer.set_validation(validation_iter, scorer) else: print("Cross-validation will not be performed.") validation_iter = None print("Training neural network.") sys.stdout.flush() trainer.train() if not "layers" in state.keys(): print( "The model has not been trained. No cross-validations were " "performed or training did not improve the model." ) elif not validation_iter is None: network.set_state(state) perplexity = scorer.compute_perplexity(validation_iter) print("Best validation set perplexity:", perplexity)
def train(args): numpy.random.seed(args.random_seed) log_file = args.log_file log_level = getattr(logging, args.log_level.upper(), None) if not isinstance(log_level, int): print("Invalid logging level requested:", args.log_level) sys.exit(1) log_format = '%(asctime)s %(funcName)s: %(message)s' if args.log_file == '-': logging.basicConfig(stream=sys.stdout, format=log_format, level=log_level) else: logging.basicConfig(filename=log_file, format=log_format, level=log_level) if args.debug: theano.config.compute_test_value = 'warn' else: theano.config.compute_test_value = 'off' theano.config.profile = args.profile theano.config.profile_memory = args.profile with h5py.File(args.model_path, 'a', driver='core') as state: if state.keys(): print("Reading vocabulary from existing network state.") sys.stdout.flush() vocabulary = Vocabulary.from_state(state) elif args.vocabulary is None: print("Constructing vocabulary from training set.") sys.stdout.flush() vocabulary = Vocabulary.from_corpus(args.training_set, args.num_classes) for training_file in args.training_set: training_file.seek(0) vocabulary.get_state(state) else: print("Reading vocabulary from {}.".format(args.vocabulary)) sys.stdout.flush() with open(args.vocabulary, 'rt', encoding='utf-8') as vocab_file: vocabulary = Vocabulary.from_file(vocab_file, args.vocabulary_format) if args.vocabulary_format == 'classes': print("Computing class membership probabilities from " "unigram word counts.") sys.stdout.flush() vocabulary.compute_probs(args.training_set) vocabulary.get_state(state) print("Number of words in vocabulary:", vocabulary.num_words()) print("Number of word classes:", vocabulary.num_classes()) print("Building neural network.") sys.stdout.flush() if args.architecture == 'lstm300' or args.architecture == 'lstm1500': architecture = Architecture.from_package(args.architecture) else: with open(args.architecture, 'rt', encoding='utf-8') as arch_file: architecture = Architecture.from_description(arch_file) network = Network(vocabulary, architecture, profile=args.profile) sys.stdout.flush() if args.unk_penalty is None: ignore_unk = False unk_penalty = None elif args.unk_penalty == 0: ignore_unk = True unk_penalty = None else: ignore_unk = False unk_penalty = args.unk_penalty num_training_files = len(args.training_set) if len(args.weights) > num_training_files: print("You specified more weights than training files.") sys.exit(1) weights = numpy.ones(num_training_files).astype(theano.config.floatX) for index, weight in enumerate(args.weights): weights[index] = weight print("Building text scorer.") scorer = TextScorer(network, ignore_unk, unk_penalty, args.profile) validation_mmap = mmap.mmap(args.validation_file.fileno(), 0, prot=mmap.PROT_READ) validation_iter = \ LinearBatchIterator(validation_mmap, vocabulary, batch_size=args.batch_size, max_sequence_length=None) optimization_options = { 'method': args.optimization_method, 'epsilon': args.numerical_stability_term, 'gradient_decay_rate': args.gradient_decay_rate, 'sqr_gradient_decay_rate': args.sqr_gradient_decay_rate, 'learning_rate': args.learning_rate, 'weights': weights, 'momentum': args.momentum, 'max_gradient_norm': args.gradient_normalization, 'cost_function': args.cost, 'num_noise_samples': args.num_noise_samples, 'ignore_unk': ignore_unk, 'unk_penalty': unk_penalty } logging.debug("OPTIMIZATION OPTIONS") for option_name, option_value in optimization_options.items(): if type(option_value) is list: value_str = ', '.join(str(x) for x in option_value) logging.debug("%s: [%s]", option_name, value_str) else: logging.debug("%s: %s", option_name, str(option_value)) training_options = { 'strategy': args.training_strategy, 'batch_size': args.batch_size, 'sequence_length': args.sequence_length, 'validation_frequency': args.validation_frequency, 'patience': args.patience, 'stopping_criterion': args.stopping_criterion, 'max_epochs': args.max_epochs, 'min_epochs': args.min_epochs, 'max_annealing_count': args.max_annealing_count } logging.debug("TRAINING OPTIONS") for option_name, option_value in training_options.items(): logging.debug("%s: %s", option_name, str(option_value)) print("Building neural network trainer.") sys.stdout.flush() if len(args.sampling) > len(args.training_set): print("You specified more sampling coefficients than training " "files.") sys.exit(1) trainer = create_trainer( training_options, optimization_options, network, vocabulary, scorer, args.training_set, args.sampling, validation_iter, state, args.profile) trainer.set_logging(args.log_interval) print("Training neural network.") sys.stdout.flush() trainer.train() if not 'layers' in state.keys(): print("The model has not been trained. No cross-validations were " "performed or training did not improve the model.") else: network.set_state(state) perplexity = scorer.compute_perplexity(validation_iter) print("Best validation set perplexity:", perplexity)
def train(args): numpy.random.seed(args.random_seed) log_file = args.log_file log_level = getattr(logging, args.log_level.upper(), None) if not isinstance(log_level, int): raise ValueError("Invalid logging level requested: " + args.log_level) log_format = '%(asctime)s %(funcName)s: %(message)s' if args.log_file == '-': logging.basicConfig(stream=sys.stdout, format=log_format, level=log_level) else: logging.basicConfig(filename=log_file, format=log_format, level=log_level) if args.debug: theano.config.compute_test_value = 'warn' else: theano.config.compute_test_value = 'off' theano.config.profile = args.profile theano.config.profile_memory = args.profile with h5py.File(args.model_path, 'a', driver='core') as state: if state.keys(): print("Reading vocabulary from existing network state.") sys.stdout.flush() vocabulary = Vocabulary.from_state(state) elif args.vocabulary is None: print("Constructing vocabulary from training set.") sys.stdout.flush() vocabulary = Vocabulary.from_corpus(args.training_set, args.num_classes) for training_file in args.training_set: training_file.seek(0) vocabulary.get_state(state) else: print("Reading vocabulary from {}.".format(args.vocabulary)) sys.stdout.flush() with open(args.vocabulary, 'rt', encoding='utf-8') as vocab_file: vocabulary = Vocabulary.from_file(vocab_file, args.vocabulary_format) if args.vocabulary_format == 'classes': print("Computing class membership probabilities from " "unigram word counts.") sys.stdout.flush() vocabulary.compute_probs(args.training_set) vocabulary.get_state(state) print("Number of words in vocabulary:", vocabulary.num_words()) print("Number of word classes:", vocabulary.num_classes()) print("Building neural network.") sys.stdout.flush() if args.architecture == 'lstm300' or args.architecture == 'lstm1500': architecture = Architecture.from_package(args.architecture) else: with open(args.architecture, 'rt', encoding='utf-8') as arch_file: architecture = Architecture.from_description(arch_file) network = Network(vocabulary, architecture, profile=args.profile) sys.stdout.flush() if args.unk_penalty is None: ignore_unk = False unk_penalty = None elif args.unk_penalty == 0: ignore_unk = True unk_penalty = None else: ignore_unk = False unk_penalty = args.unk_penalty num_training_files = len(args.training_set) if len(args.weights) > num_training_files: print("You specified more weights than training files.") sys.exit(1) weights = numpy.ones(num_training_files).astype(theano.config.floatX) for index, weight in enumerate(args.weights): weights[index] = weight print("Building text scorer.") scorer = TextScorer(network, ignore_unk, unk_penalty, args.profile) validation_mmap = mmap.mmap(args.validation_file.fileno(), 0, prot=mmap.PROT_READ) validation_iter = LinearBatchIterator(validation_mmap, vocabulary, batch_size=32) optimization_options = { 'method': args.optimization_method, 'epsilon': args.numerical_stability_term, 'gradient_decay_rate': args.gradient_decay_rate, 'sqr_gradient_decay_rate': args.sqr_gradient_decay_rate, 'learning_rate': args.learning_rate, 'weights': weights, 'momentum': args.momentum, 'max_gradient_norm': args.gradient_normalization, 'ignore_unk': ignore_unk, 'unk_penalty': unk_penalty } logging.debug("OPTIMIZATION OPTIONS") for option_name, option_value in optimization_options.items(): if type(option_value) is list: value_str = ', '.join(str(x) for x in option_value) logging.debug("%s: [%s]", option_name, value_str) else: logging.debug("%s: %s", option_name, str(option_value)) training_options = { 'strategy': args.training_strategy, 'batch_size': args.batch_size, 'sequence_length': args.sequence_length, 'validation_frequency': args.validation_frequency, 'patience': args.patience, 'stopping_criterion': args.stopping_criterion, 'max_epochs': args.max_epochs, 'min_epochs': args.min_epochs, 'max_annealing_count': args.max_annealing_count } logging.debug("TRAINING OPTIONS") for option_name, option_value in training_options.items(): logging.debug("%s: %s", option_name, str(option_value)) print("Building neural network trainer.") sys.stdout.flush() if len(args.sampling) > len(args.training_set): print("You specified more sampling coefficients than training " "files.") sys.exit(1) trainer = create_trainer( training_options, optimization_options, network, vocabulary, scorer, args.training_set, args.sampling, validation_iter, state, args.profile) trainer.set_logging(args.log_interval) print("Training neural network.") sys.stdout.flush() trainer.run() if not state.keys(): print("The model has not been trained.") else: network.set_state(state) perplexity = scorer.compute_perplexity(validation_iter) print("Best validation set perplexity:", perplexity)
def train(args): """A function that performs the "theanolm train" command. :type args: argparse.Namespace :param args: a collection of command line arguments """ numpy.random.seed(args.random_seed) log_file = args.log_file log_level = getattr(logging, args.log_level.upper(), None) if not isinstance(log_level, int): print("Invalid logging level requested:", args.log_level) sys.exit(1) log_format = '%(asctime)s %(funcName)s: %(message)s' if args.log_file == '-': logging.basicConfig(stream=sys.stdout, format=log_format, level=log_level) else: logging.basicConfig(filename=log_file, format=log_format, level=log_level) if args.debug: theano.config.compute_test_value = 'warn' logging.info("Enabled computing test values for tensor variables.") logging.warning("GpuArray backend will fail random number generation!") else: theano.config.compute_test_value = 'off' theano.config.profile = args.profile theano.config.profile_memory = args.profile with h5py.File(args.model_path, 'a', driver='core') as state: vocabulary = _read_vocabulary(args, state) if args.num_noise_samples > vocabulary.num_classes(): print("Number of noise samples ({}) is larger than the number of " "classes. This doesn't make sense and would cause unigram " "sampling to fail.".format(args.num_noise_samples)) sys.exit(1) num_training_files = len(args.training_set) if len(args.weights) > num_training_files: print("You specified more weights than training files.") sys.exit(1) weights = numpy.ones(num_training_files).astype(theano.config.floatX) for index, weight in enumerate(args.weights): weights[index] = weight if len(args.sampling) > num_training_files: print("You specified more sampling coefficients than training " "files.") sys.exit(1) training_options = { 'batch_size': args.batch_size, 'sequence_length': args.sequence_length, 'validation_frequency': args.validation_frequency, 'patience': args.patience, 'stopping_criterion': args.stopping_criterion, 'max_epochs': args.max_epochs, 'min_epochs': args.min_epochs, 'max_annealing_count': args.max_annealing_count } optimization_options = { 'method': args.optimization_method, 'epsilon': args.numerical_stability_term, 'gradient_decay_rate': args.gradient_decay_rate, 'sqr_gradient_decay_rate': args.sqr_gradient_decay_rate, 'learning_rate': args.learning_rate, 'weights': weights, 'momentum': args.momentum, 'max_gradient_norm': args.gradient_normalization, 'num_noise_samples': args.num_noise_samples, 'noise_sharing': args.noise_sharing, } log_options(training_options, optimization_options, args) logging.info("Creating trainer.") trainer = Trainer(training_options, vocabulary, args.training_set, args.sampling) trainer.set_logging(args.log_interval) logging.info("Building neural network.") if args.architecture == 'lstm300' or args.architecture == 'lstm1500': architecture = Architecture.from_package(args.architecture) else: with open(args.architecture, 'rt', encoding='utf-8') as arch_file: architecture = Architecture.from_description(arch_file) default_device = get_default_device(args.default_device) network = Network(architecture, vocabulary, trainer.class_prior_probs, default_device=default_device, profile=args.profile) network.set_sampling(args.noise_distribution, args.noise_dampening, args.noise_sharing) logging.info("Building optimizer.") exclude_id = vocabulary.word_to_id['<unk>'] if args.exclude_unk \ else None epsilon = args.numerical_stability_term if args.cost == 'cross-entropy': cost_function = CrossEntropyCost(network, exclude_id, args.l1_regularization, args.l2_regularization, epsilon) elif args.cost == 'nce': cost_function = NCECost(network, exclude_id, args.l1_regularization, args.l2_regularization, epsilon) else: assert args.cost == 'blackout' cost_function = BlackoutCost(network, exclude_id, args.l1_regularization, args.l2_regularization, epsilon) try: optimizer = create_optimizer(optimization_options, network, cost_function, profile=args.profile) except theano.gradient.DisconnectedInputError as e: print("Cannot train the neural network because some of the " "parameters are disconnected from the output. Make sure all " "the layers are correctly connected in the network " "architecture. The error message was: `{}ยด".format(e)) if args.print_graph: print("Cost function computation graph:") theano.printing.debugprint(optimizer.gradient_update_function) trainer.initialize(network, state, optimizer, args.load_and_train) if args.validation_file is not None: logging.info("Building text scorer for cross-validation.") scorer = TextScorer(network, use_shortlist=True, exclude_unk=args.exclude_unk, profile=args.profile) logging.info("Validation text: %s", args.validation_file.name) validation_mmap = mmap.mmap(args.validation_file.fileno(), 0, prot=mmap.PROT_READ) validation_iter = \ LinearBatchIterator(validation_mmap, vocabulary, batch_size=args.batch_size, max_sequence_length=args.sequence_length, map_oos_to_unk=False) trainer.set_validation(validation_iter, scorer) else: logging.info("Cross-validation will not be performed.") validation_iter = None logging.info("Training neural network.") trainer.train() if 'layers' not in state.keys(): print("The model has not been trained. No cross-validations were " "performed or training did not improve the model.") elif validation_iter is not None: network.set_state(state) perplexity = scorer.compute_perplexity(validation_iter) print("Best validation set perplexity:", perplexity)
def test_score_batch(self): # Network predicts <unk> probability. Out-of-shortlist words are mapped # to <unk> class by . scorer = TextScorer(self.dummy_network, use_shortlist=False) word_ids = numpy.arange(15).reshape((3, 5)).T class_ids, _ = self.vocabulary.get_class_memberships(word_ids) membership_probs = numpy.ones_like(word_ids).astype('float32') mask = numpy.ones_like(word_ids) logprobs = scorer.score_batch(word_ids, class_ids, membership_probs, mask) assert_almost_equal( logprobs[0], numpy.log(word_ids[1:, 0].astype('float32') / 100.0)) assert_almost_equal( logprobs[1], numpy.log(word_ids[1:, 1].astype('float32') / 100.0)) self.assertAlmostEqual(logprobs[2][0], numpy.log(11.0 / 100.0), places=5) # </s> self.assertAlmostEqual(logprobs[2][1], numpy.log(12.0 / 100.0), places=5) # <unk> self.assertAlmostEqual(logprobs[2][2], numpy.log(12.0 / 100.0), places=5) self.assertAlmostEqual(logprobs[2][3], numpy.log(12.0 / 100.0), places=5) # Network predicts <unk> probability. This is distributed for # out-of-shortlist words according to word frequency. scorer = TextScorer(self.dummy_network, use_shortlist=True) word_ids = numpy.arange(15).reshape((3, 5)).T class_ids, _ = self.vocabulary.get_class_memberships(word_ids) membership_probs = numpy.ones_like(word_ids).astype('float32') mask = numpy.ones_like(word_ids) logprobs = scorer.score_batch(word_ids, class_ids, membership_probs, mask) assert_almost_equal( logprobs[0], numpy.log(word_ids[1:, 0].astype('float32') / 100.0)) assert_almost_equal( logprobs[1], numpy.log(word_ids[1:, 1].astype('float32') / 100.0)) self.assertAlmostEqual(logprobs[2][0], numpy.log(11.0 / 100.0), places=5) # </s> self.assertIsNone(logprobs[2][1]) # <unk> self.assertAlmostEqual(logprobs[2][2], numpy.log(12.0 / 100.0 * 0.3), places=5) self.assertAlmostEqual(logprobs[2][3], numpy.log(12.0 / 100.0 * 0.7), places=5) # OOV and OOS words are replaced with None. scorer = TextScorer(self.dummy_network, use_shortlist=False, exclude_unk=True) word_ids = numpy.arange(15).reshape((3, 5)).T class_ids, _ = self.vocabulary.get_class_memberships(word_ids) membership_probs = numpy.ones_like(word_ids).astype('float32') mask = numpy.ones_like(word_ids) logprobs = scorer.score_batch(word_ids, class_ids, membership_probs, mask) assert_almost_equal( logprobs[0], numpy.log(word_ids[1:, 0].astype('float32') / 100.0)) assert_almost_equal( logprobs[1], numpy.log(word_ids[1:, 1].astype('float32') / 100.0)) self.assertAlmostEqual(logprobs[2][0], numpy.log(11.0 / 100.0), places=5) # </s> self.assertIsNone(logprobs[2][1]) # <unk> self.assertIsNone(logprobs[2][2]) self.assertIsNone(logprobs[2][3])